The Life Cycle State Evaluation of Electrical Equipment based on Digital Twins

Nowadays, with the development of smart technologies, more requirements for the state evaluation of electrical equipment have been raised during the full life cycle. In order to meet these demands, the digital twin is a very suitable technology. The digital twins could understand the state of the physical entities through physical models, sensing data and the big data generated from the product lifecycle, so as to predict, estimate, and analyze the dynamic changes. The digital twin is mainly composed of the information data, data exchange interface and digital model. The information data is obtained from the equipment production, operation and management process, including equipment production information and CAD model, environmental information, pre-test and maintenance data, online monitoring data and various sensor measurement data. It provides a comprehensive description of the actual equipment. The data exchange interface is a standardized information transmission channel, which ensures timely and efficient data transmission at various time periods throughout the life cycle of the equipment. The digital model acquires the date through the date exchange interface, structurally stores the data, establishes a virtual model corresponding to the equipment. In order to build up the digital twin, two main problems should be solved. One is the acquisition and storage of the data, the other is the responding speed of the digital model. In the current equipment management system, the various information data of the equipment entire life cycle are independent of each other, and the data utilization efficiency is quite low. Thus, we put forward the life cycle digital thread for the electrical equipment. It contains not only the data of design, production, installation and operation, but also the virials sensing data, environment date and historical experience data of similar equipment. To solve the problem of the responding speed, the preparatory simulation model has been put forward. In this model, a series of the equipment statues are simulated forward and the results are stored in the digital twin. Based on these simulation results and the simplified physical model, the relationship of the sensing data and the equipment state is build. Then, the relationship is revised and optimized through experiment to make sure of the accuracy of the model. This paper studies the data modelling methods and applies the “digital twin” to the electrical equipment. It is of great significance for the development of intelligent electrical equipment.